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The classification problem in the image processing field is an important challenge, so that in the process image pixels are separated into previously determined classes according to their features. Th...
The classification problem in the image processing field is an important challenge, so that in the process image pixels are separated into previously determined classes according to their features. Th...
Polarimetric Synthetic Aperture Radar (PolSAR) imagery is a complex multi-dimensional dataset, which is an important source of information for various natural resources and environmental classificatio...
The hyperspectral image enriches spectrum information, so compared with panchromatic image and multispectral image; it can classify the ground target better. The feature extraction of hyperspectral im...
Hyperspectral images provide abundant information about objects. The high dimensionality of such images arise various problems such as curse of dimensionality and large hypothesis space. There are tw...
Fisher linear discriminant analysis (LDA) can be sensitive to the problem data. Robust Fisher LDA can systematically alleviate the sensitivity problem by explicitly incorporating a model of data uncer...
In Kernel Fisher discriminant analysis (KFDA), we carry out Fisher linear discriminant analysis in a high dimensional feature space defined implicitly by a kernel. The performance of KFDA depends on t...
Discriminant analysis, especially Fisherface and its numerous variants, have achieved great success in face recognition. However, these methods fail to work for face recognition from Single Sample per...
This paper presents a novel discriminative learning method, called Manifold Discriminant Analysis (MDA), to solve the problem of image set classification. By modeling each image set as a manifold, we...
Linear Discriminant Analysis (LDA) is a popular method for dimensionality reduction and classification. In real-world applications when there is no sufficient labeled data, LDA suffers from serious ...
Linear Discriminant Analysis (LDA) is a popular method for dimensionality reduction and classication. In real-world applications when there is no sufficient labeled data, LDA suffers fro...
收集国内常用的、具有代表性的精料补充料和肉骨粉,制备定标集样品235个,外部验证集样品152个。在400~2498nm波长范围内进行近红外光谱扫描,选择合适的光谱预处理方法和光谱范围,采用SIMCA方法和DPLS方法分别建立判别分析模型。建立的SIMCA判别分析模型对外部验证集的正确判断率为96.1%,建立的DPLS判别分析模型对外部验证集的正确判断率为100%。与NIRS定量分析精料补充料中MB...
This paper presents a novel discriminative learning method, called Manifold Discriminant Analysis (MDA), to solve the problem of image set classification. By modeling each image set as a manifold, we ...
Traditional discriminate analysis treats all the involved classes equally in the computation of the between-class scatter matrix. However, we find that for many vision tasks, the classes to be process...
Parzen Discriminant Analysis     Parzen  Discriminant Analysis       2010/12/17
In this paper, we propose a non-parametric Discriminant Analysis method (no assumption on the distributions of classes), called Parzen Discriminant Analysis (PDA). Through a deep investigation on the...

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